Conditional Random Fields (CRF). We use CRF as a structured classifier to model sequences observed in Twitter conversations. With CRF, we can model the conversation as a graph that will be treated as a sequence of stances, which also enables us to assess the utility of harnessing the conversational structure for stance classification. In contrast to traditionally used classifiers for this task, which choose a label for each input unit (e.g. a tweet), CRF also consider the neighbours of each unit, learning the probabilities of transitions of label pairs to be followed by each other. The input for CRF is a graph \(G = (V, E)\) , where in our case each of the vertices \(V\)  is a tweet, and the edges \(E\)  are relations of tweets replying to each other. Hence, having a data sequence \(X\)  as input, CRF outputs a sequence of labels \(Y\)  [1]}, where the output of each element \(y_i\)  will not only depend on its features, but also on the probabilities of other labels surrounding it. The generalisable conditional distribution of CRF is shown in Equation REF  [2]}.
\(p(y|x) = \frac{1}{Z(x)} \prod _{a = 1}^{A} \Psi _a (y_a, x_a)\) 
